Development and Validation of KN-DIOC: A Novel Preoperative Diagnostic Index Using Ultrasound,
Complete Blood Count, and Cancer Antigen 125 for Ovarian Cancer

ing CBC, ultrasound features, and tumor markers, provides a robust
tool for preoperative assessment of ovarian tumors in Thai patients.
It offers significant improvements in sensitivity and specificity over
existing models, suggesting its potential for broader application in
similar settings. This index supports enhanced decision-making in gynecological oncology, potentially leading to better patient outcomes
through timely and accurate diagnosis.   Background: Ovarian cancer, particularly epithelial ovarian cancer
(EOC), is one of the deadliest gynecological malignancies due to nonspecific early symptoms and late diagnosis. Current diagnostic tools,
while useful, often do not account for regional variations in disease
presentation, particularly in Asian populations. This study aimed to
develop and validate a new preoperative diagnostic index tailored to
the Thai population by integrating complete blood count (CBC), tumor markers, and ultrasound features.
Methods: This retrospective cohort study included patients with
pathologic pelvic or adnexal masses scheduled for surgery at Vajira
Hospital from April 2022 to October 2024. Clinical data, CBC, cancer
antigen 125 (CA125) levels, and ultrasound findings were analyzed
to develop and validate a diagnostic index (KMUTT-NMU Diagnostic Index for Ovarian Cancer (KN-DIOC)). The model’s performance
was compared against established indices like Risk of Malignancy
Index (RMI), Risk of Ovarian Malignancy Algorithm (ROMA), and
Rajavithi-Ovarian Cancer Predictive Score (R-OPS) through multivariate logistic regression, focusing on key predictors.
Results: The study comprised 195 patients divided into 151 for the
development dataset and 44 for the validation dataset. The KN-DIOC
showed high discriminative ability with an area under curve (AUC) of
0.866, indicating very good capability in differentiating between benign and malignant ovarian masses. The index achieved a sensitivity
of 93.75% and a specificity of 78.57%, demonstrating superior performance to traditional diagnostic tools, especially in the validation dataset.
Conclusion: The novel diagnostic index (KN-DIOC), incorporat